Etch metric sensitivity for endpoint detection

ABSTRACT

Monitoring a geometric parameter value for one or more features produced on a substrate during an etch process may involve: (a) measuring optical signals produced by optical energy interacting with features being etched on the substrate; (b) providing a subset of the measured optical signals, wherein the subset is defined by a range where optical signals were determined to correlate with target geometric parameter values for features; (c) applying the subset of optical signals to a model configured to predict the target geometric parameter values from the measured optical signals; (d) determining, from the model, a current value of the target geometric parameter of the features being etched; (e) comparing the current value of the target geometric parameter of the features being etched to an etch process endpoint value for the target geometric parameter; and (f) repeating (a)-(e) until the comparing in (e) indicates that the current value of the target geometric parameter of the features being etched has reached the endpoint value.

BACKGROUND

High performance plasma-assisted etch processes are important to thesuccess of many semiconductor processing workflows. However, monitoring,controlling, and/or optimizing the etch processes can be difficult andtime-consuming, oftentimes involving process engineers laboriouslytesting etch process parameters to empirically determine settings thatproduce a target etch profile. Additionally, in situ monitoring of etchprocesses can be difficult and unreliable; etch endpoint detectionremains a challenge.

Computational models might be useful to facilitate designing andmonitoring etch processes. Some models attempt to simulate thephysical-chemical processes occurring on semiconductor substratesurfaces during etch processes. Examples include the etch profile modelsof M. Kushner and co-workers as well as the etch profile models ofCooperberg and co-workers. The former are described in Y. Zhang, “LowTemperature Plasma Etching Control through Ion Energy AngularDistribution and 3-Dimensional Profile Simulation,” Chapter 3,dissertation, University of Michigan (2015), and the latter inCooperberg, Vahedi, and Gottscho, “Semiempirical profile simulation ofaluminum etching in a Cl₂/BCl₃ plasma,” J. Vac. Sci. Technol. A 20(5),1536 (2002), each of which is hereby incorporated by reference in itsentirety for all purposes. Additional description of the etch profilemodels of M. Kushner and co-workers may be found in J. Vac. Sci.Technol. A 15(4), 1913 (1997), J. Vac. Sci. Technol. B 16(4), 2102(1998), J. Vac. Sci. Technol. A 16(6), 3274 (1998), J. Vac. Sci.Technol. A 19(2), 524 (2001), J. Vac. Sci. Technol. A 22(4), 1242(2004), J. Appl. Phys. 97, 023307 (2005), each of which is also herebyincorporated by reference in its entirety for all purposes. Despite theextensive work done to develop these models, they do not yet possess thedesired degree of accuracy and reliability to find substantial usewithin the semiconductor processing industry.

SUMMARY

One aspect of this disclosure pertains to methods of monitoring ordetermining a geometric parameter value for one or more featuresproduced on a substrate during an etch process. Such methods may becharacterized by the following operations: (a) measuring optical signalsproduced by optical energy interacting with features being etched on thesubstrate; (b) providing a subset of the measured optical signals,wherein the subset is defined by a range where optical signals weredetermined to correlate with target geometric parameter values forfeatures; (c) applying the subset of optical signals to a modelconfigured to predict the target geometric parameter values from themeasured optical signals; (d) determining, from the model, a currentvalue of the target geometric parameter of the features being etched;(e) comparing the current value of the target geometric parameter of thefeatures being etched to an etch process endpoint value for the targetgeometric parameter; and (f) repeating (a)-(e) until the comparing in(e) indicates that the current value of the target geometric parameterof the features being etched has reached the endpoint value. In certainembodiments, the model was generated by determining the range whereoptical signals were determined to correlate with target geometricparameter values for features. In certain embodiments, the targetgeometric parameter of the features being etched is an etch depth, apitch, or an etch critical dimension.

In some implementations, the method includes the additional operation ofterminating the etch process when the comparing in (e) indicates thatthe current value of the target geometric parameter of the featuresbeing etched has reached the endpoint value. In certain embodiments, theoperation of measuring optical signals produced in (a) includesmeasuring reflectance produced from the features being etched on thesubstrate.

In certain embodiments, the range defining the subset of measuredoptical signals in (b) is a range of wavelengths where the opticalsignals were determined to correlate with the target geometric parametervalue for the features. In certain embodiments, the range defining thesubset of measured optical signals in (b) varies between two repetitionsof (a)-(e). In some cases, the range defining the subset of measuredoptical signals in (b) was determined to vary according to variations incorrelation of the optical signals with the target geometric parameterfor different values of the target geometric parameter. In someimplementations, the range defining the subset of measured opticalsignals in (b) is a range where the optical signals were determined tocorrelate less strongly with a non-target geometric parameter than thetarget geometric parameter.

Certain aspects of the present disclosure pertain to systems for etchingone or more features on a substrate during an etch process. Such systemsmay be characterized by the following features: an etching apparatus foretching semiconductor substrates; and a controller for controlling theoperation of the etching apparatus. The controller includesnon-transitory memory storing executable instructions for (a) measuringoptical signals produced by optical energy interacting with featuresbeing etched on the substrate; (b) providing a subset of the measuredoptical signals, wherein the subset is defined by a range where opticalsignals were determined to correlate with target geometric parametervalues for features; (c) applying the subset of optical signals to amodel configured to predict the target geometric parameter values fromthe measured optical signals; (d) determining, from the model, a currentvalue of the target geometric parameter of the features being etched;(e) comparing the current value of the target geometric parameter of thefeatures being etched to an etch process endpoint value for the targetgeometric parameter; and (f) repeating (a)-(e) until the comparing in(e) indicates that the current value of the target geometric parameterof the features being etched has reached the endpoint value. In certainembodiments, the model was generated by determining the range whereoptical signals were determined to correlate with target geometricparameter values for features. In some implementations, the targetgeometric parameter of the features being etched is an etch depth, apitch, or an etch critical dimension

In some implementations, the etching apparatus includes: (i) aprocessing chamber; (ii) a substrate holder for holding a substratewithin the processing chamber; (iii) a plasma generator for generating aplasma within the processing chamber, the plasma generator comprising anRF power supply; (iv) one or more valve-controlled process gas inletsfor flowing one or more process gases into the processing chamber; and(v) one or more gas outlets fluidically connected to one or more vacuumpumps for evacuating gases from the processing chamber.

In certain embodiments, the controller further includes instructions forterminating the etch process when the comparing in (e) indicates thatthe current value of the target geometric parameter of the featuresbeing etched has reached the endpoint value. In some embodiments, thecontroller's instructions for measuring optical signals produced in (a)include instructions for measuring reflectance produced from thefeatures being etched on the substrate.

In some implementations, the controller includes instructions forvarying the range defining the subset of measured optical signals in (b)between two repetitions of executing instructions for (a)-(e). In suchimplementations, the range defining the subset of measured opticalsignals in (b) may have been determined to vary according to variationsin correlation of the optical signals with the target geometricparameter for different values of the target geometric parameter.

In certain embodiments, the range defining the subset of measuredoptical signals in (b) is a range where the optical signals weredetermined to correlate less strongly with a non-target geometricparameter than the target geometric parameter. In some implementations,the range defining the subset of measured optical signals in (b) is arange of wavelengths where the optical signals were determined, using aregression technique, to correlate with the target geometric parametervalue for the features. Another aspect of the disclosure pertains tomethods of generating a computational model that relates measuredoptical signals produced by optical energy interacting with featuresetched on a substrate to values of a target geometric parameter of thefeatures etched on the substrate. Such methods may be characterized bythe following features: (a) determining a range where the measuredoptical signals correlate less strongly with a non-target geometricparameter than with the target geometric parameter; (b) providing atraining set having members with values of the optical signals in therange, wherein each member of the training set comprises (i) a value ofthe target geometric parameter of the features etched in the substrate,and (ii) an associated optical signal produced from etched featureshaving the value of the target geometric parameter of the featuresetched in the substrate; and (c) producing the computational model fromthe training set.

In some embodiments, the target geometric parameter of the featuresetched on the substrate is an etch depth, a pitch, or an etch criticaldimension. In some embodiments, the optical signals comprise reflectanceproduced from the features etched on the substrate. In someimplementations, the range where the measured optical signals correlateless strongly with a non-target geometric parameter than with the targetgeometric parameter is a range of wavelengths. In certain embodiments,determining the range involves determining variations in the rangeaccording to variations in correlation of the optical signals with thetarget geometric parameter for different values of the target geometricparameter. In certain embodiments, producing the computational modelfrom the training set involves using a neural network or a regressiontechnique.

In some examples, the training set includes at least about 50 members.In certain embodiments, the members of the training set additionallyinclude a value of a non-target geometric parameter of the featuresetched in the substrate. In some implementations, the members of thetraining set are obtained experimentally. In some implementations, themembers of the training set are generated computationally. In suchcases, the members of the training set may be generated from a surfacekinetic model and an optical modelling routine.

Another aspect of the present disclosure pertains to computationalmodels configured to calculate target geometric parameter values forfeatures etched on a substrate from measured optical signals produced byoptical energy interacting with the features etched on the substrate.Such models may be generated by a method as presented above.

For example, the members of the training set used to generate thecomputational model may include values of a non-target geometricparameter of the features etched in the substrate. Further, the membersof the training set used to generate the computational model may beobtained experimentally or generated computationally such as from asurface kinetic model and an optical modelling routine. In someimplementations, the training set included at least about 50 members.Further, the computational model may be produced from the training setusing a neural network or a regression technique.

In some implementations, the computational model predicts a targetgeometric parameter of the features etched on the substrate, whichfeatures may be, for example, etch depth, pitch, or etch criticaldimension. In some models, the optical signals include reflectancevalues produced from the features etched on the substrate.

In certain embodiments, when generating the computational model, therange where the measured optical signals correlate less strongly with anon-target geometric parameter than with the target geometric parameteris a range of wavelengths. In certain embodiments, when generating thecomputational model, determining the range comprises determiningvariations in the range according to variations in correlation of theoptical signals with the target geometric parameter for different valuesof the target geometric parameter.

These and other features of the disclosed embodiments will be set forthin more detail below, with reference to the associated drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the evolution of a feature during an etch process.

FIG. 2 presents an example of how an optical parameter (e.g., reflectedsignal intensity at particular direction) can vary with a feature ofinterest (in this case etch depth) and consequently vary with etch time.

FIG. 3 presents a process for monitoring an etch process and makingadjustments if necessary.

FIG. 4 presents a flow chart of a method for generating models inaccordance with certain embodiments.

FIGS. 5A-5C illustrate an embodiment of an adjustable gap capacitivelycoupled confined RF plasma reactor.

FIG. 6 illustrates a cross-sectional view of an inductively coupledplasma etching apparatus appropriate for implementing certainembodiments herein, an example of which is a Kiyo™ reactor, produced byLam Research Corp. of Fremont, Calif.

DETAILED DESCRIPTION

Introduction and Context

In this application, the terms “semiconductor wafer,” “wafer,”“substrate,” “wafer substrate,” and “partially fabricated integratedcircuit” are used interchangeably. One of ordinary skill in the artwould understand that the term “partially fabricated integrated circuit”can refer to a silicon wafer during any of many stages of integratedcircuit fabrication thereon. A wafer used in the semiconductor deviceindustry typically has a diameter of 200 mm, or 300 mm, or 450 mm. Thefollowing detailed description assumes the invention is implemented on awafer. However, the invention is not so limited. The work piece may beof various shapes, sizes, and materials. In addition to semiconductorwafers, other work pieces that may take advantage of this inventioninclude various articles such as printed circuit boards, magneticrecording media, magnetic recording sensors, mirrors, optical elementsincluding pixelated displays, micro-mechanical devices and the like.

Fabrication of certain semiconductor devices involves etching featuresinto a material or materials. The material may be a single layer ofmaterial or a stack of materials. In some cases a stack includesalternating layers of material (e.g., silicon nitride and siliconoxide). One example etched feature is a cylinder.

In various embodiments herein, features are etched in a substrate(typically a semiconductor wafer) having dielectric, semiconductor,and/or conductor material on the surface. The etching processes aregenerally plasma-based etching processes. A feature is a recess in thesurface of a substrate. Features can have many different shapesincluding, but not limited to, cylinders, rectangles, squares, otherpolygonal recesses, trenches, etc. Examples of etched features includevarious gaps, holes or vias, trenches, and the like.

The following disclosure includes (1) methods and apparatus forgenerating computationally efficient models for determining the etchdepth or other geometric parameter characterizing features produced inan etch process from a limited range of wavelengths or othertime-dependent optical signal generated by in situ optical metrologyequipment, and (2) models that receive a selected range oftime-dependent optical signals detected by in situ metrology and usethose selected optical signals to calculate the depth and/or othergeometric parameter of features in a substrate undergoing etch. Incertain embodiments, the features are periodic or repeating structures,such as those commonly produced for memory. While the methods andapparatus of (1) may be used to generate the models in (2), the modelsare not limited to those produced by such methods and apparatus. Incertain embodiments, the models of (2) are generated using the processesof (1). In certain embodiments, the model is coded or otherwiseimplemented in an apparatus such that when it executes it providesreal-time monitoring of the etch process in an etch apparatus. In someimplementations, the model determines or assists in determining theendpoint of the etch process.

The models may be prepared from data generated empirically and/orcomputationally. In some embodiments, the data is computationallygenerated from (1) a surface kinetic model or similar model thatpredicts etch feature geometry parameters (e.g., etch profiles) fromreactor etch conductions (chemical and/or physical), and (2) an opticalmodeling routine that predicts optical signals (e.g.,wavelength-dependent reflectance) from repeating feature geometries. Insuch embodiments, selected etch conditions are input to the first model,which produces predicted feature geometries, which are in turn providedto the optical modeling routine, which predicts optical signals thatwould be produced by the feature geometries, and hence the input etchconditions. In this way, data linking optical signal values to featuregeometries is generated. An etch process modeled and/or monitored asdescribed herein may be characterized by various features. For example,the process may be characterized by the type of material or substratebeing etched. The etched material may be a conductor, a dielectric, asemiconductor, or any combination thereof. Further, the etched materialmay be monolithic or layered. It may be used to form, memory and/orlogic devices. Examples of dielectric materials for etching includesilicon oxides, silicon nitrides, silicon carbides, oxynitrides,oxycarbides, carbo-nitrides, doped versions of these materials (e.g.,doped with boron, phosphorus, etc.), and laminates from any combinationsof these materials. Particular example materials include stoichiometricand non-stoichiometric formulations of Si02, SiN, SiON, SiOC, SiCN, etc.Examples of conductor materials include, but are not limited to,nitrides such as titanium nitride and tantalum nitride and metals suchas cobalt, aluminum, ruthenium, hafnium, titanium, tungsten, platinum,iridium, palladium, manganese, nickel, iron, silver, copper, molybdenum,tin, and various alloys, including alloys of these metals. Examples ofsemiconductor materials include, but are not limited to, doped andundoped silicon, germanium, gallium arsenide, etc. Any of the aboveconductors, semiconductors, and dielectrics may have a distinctmorphology such as polycrystalline, amorphous, single crystal, and/ormicrocrystalline. Other materials that may be etched include, but notlimited to, CoFeB, Ge₂Sb₂Te₂, InSbTe compounds, Ag—Ge—S compounds, andCu—Te—S compounds. The concept can be extended to materials like NiOx,SrTiOx, perovskite (CaTiO₃), PrCaMnO₃, PZT (PbZr_(1-x)Ti_(x)O₃),(SrBiTa)O₃, and the like.

The apparatus and plasma conditions disclosed herein may be employed toetch features in devices or other structures at any technology node. Insome embodiments, the etch is used during fabrication of in the 20-10 nmnodes or beyond. Etching can be used in front end of line fabricationprocedures and/or back end of line fabrication procedures.

The etch process may be primarily physical (e.g., non-reactive ionbombardment), primarily chemical (e.g., chemical radicals with onlysmall directional bombardment), or any combination thereof. When achemical etch is included, the chemical reactant may be any one or moreof a variety of etchants including, for example, reactants containingfluorocarbons, fluorine, oxygen, chlorine, etc. Example etchants includechlorine (Cl₂), boron trichloride (BCl₃), sulfur hexafluoride (SF₆),nitrogen trifluoride (NF₃), dichlorodifluoromethane (CCl₂F₂), phosphorustrifluoride (PF₃), trifluoromethane (CHF₃), carbonyl fluoride (COF₂),oxygen (O₂), carbon tetrachloride (CCl₄), silicon tetrachloride (SiCl₄),carbon monoxde (CO), nitric oxide (NO), methanol (CH₃OH), ethanol(C₂H₅OH), acetylacetone (C₅H₈O₂), hexafluoroacetylacetone (C₅H₂F₆O₂),thionyl chloride (SOCl₂), thionyl fluoride (SOF₂), acetic acid(CH₃COOH), pyridine (C₅H₅N), formic acid (HCOOH), and combinationsthereof. In various embodiments, a combination of these etchingreactants is used.

Many types of apparatus are suitable for conducting etch processes thatare modeled and/or controlled in accordance with one or more methodsand/or apparatus described herein. Examples of such apparatus includeinductively coupled plasma reactors and capacitively coupled plasmareactors as described below. In some embodiments, the etch process iscoupled with a deposition process (sometimes in a single reactor).Examples of such coupled deposition and etch processes include processesthat employ a sidewall protective layer to produce high aspect ratiofeatures (see e.g., U.S. patent application Ser. No. 14/560,414, filedDec. 4, 2014, U.S. patent application Ser. No. 14/724,574, filed May 28,2015, and U.S. patent application Ser. No. 14/697,521, filed Apr. 27,2015 (each of which is incorporated herein by reference in itsentirety)). Examples of atomic layer etching processes are described inU.S. Pat. Nos. 8,883,028 and 8,808,561, and U.S. patent application Ser.No. 14/696,254, filed Apr. 24, 2015, each of which is incorporatedherein by reference in its entirety.

The features being etched using a process modeled and/or or monitored asdisclosed herein may be characterized by any of various geometricparameters.

Etch depth—This represents the distance between the bottom of an etchedfeature and a substrate top surface plane such as a field region. Theparameter “h” shown in FIG. 1 represents the etch depth of a feature 101etched in a layer 103 on a substrate 105. Examples of etched featureshaving a depth include holes such as cylinders and trenches. In someimplementations, the etch depth is compared in real time to an endpointdepth for an etch process being monitored. As examples, the featuresbeing etched have, at the conclusion of the etch process, a depth ofbetween about 10 nm and 1 μm.

Critical dimension—This represents the width of an unetched portionbetween sidewalls of adjacent etched features. The parameter “CD” shownin FIG. 1 represents examples of critical dimensions of a line 107.Typically, the critical dimension is function of the depth below thesubstrate top surface plane. As examples, the features being etched mayhave, at the conclusion of the etch process, a critical dimension ofbetween about 10 nm to 100 μm.

Line width—This represents the width of a raised feature between two ormore etch regions. Typically, the line width is defined by thecorresponding mask feature width, and unlike the critical dimension, itdoes not vary with depth. The parameter “w” shown in FIG. 1 representsthe line width of line 107.

Pitch—This represents the distance between center points of adjacentparallel lines. In FIG. 1, the parameter “pitch” represents the etchprofile's pitch.

Space critical dimension—This represents the difference between thepitch and the line width. It can be viewed as the width of the etchopening.

Aspect ratio—This represents the ratio of etch depth to the spacecritical dimension. It may be viewed as a measure of the thinness of anetched feature. As an example, a cylinder having a depth of 2 μm and aspace critical dimension of 50 nm has an aspect ratio of 40:1, oftenstated more simply as 40. Shallow features have relatively small aspectratios, and deep features have relatively large aspect ratios. Thefeatures formed through etch processes relating to the disclosedembodiments may be high aspect ratio features. In some applications, ahigh aspect ratio feature is one having an aspect ratio of at leastabout 5, at least about 10, at least about 20, at least about 30, atleast about 40, at least about 50, at least about 60, at least about 80,or at least about 100. The space critical dimension of the featuresformed through the disclosed methods may be about 200 nm or less, forexample about 100 nm or less, about 50 nm or less, or about 20 nm orless.

FIG. 1 illustrates the evolution of a feature during an etch process. Inthe top panel, the etch process has just begun, and the etch depth “h”into layer 103 is small. The line width “w” is defined by a lithographymask and, ideally, does not change during the etch process. In thesecond panel from the top, the etch process has proceeded further todefine a more pronounced feature 101 in layer 103. In the lower panel,the etch process has completed and feature 101 reaches the top of anunderlying substrate 105. Of course, the completed etch need not reachan underlying substrate, nor need it stop at such substrate.

Various types of optical signals may be measured to obtain informationabout the etched features. Such signals may be measured before, during,and/or after the etch process.

In certain embodiments, reflectance is measured. Reflectance is ameasure of the intensity of radiation reflected from the substrate. Thereflected signal may be captured at any angle, from normal to grazingwith respect to the substrate surface, regardless of the angle ofincidence. The reflected signal may be measured over a range ofwavelengths or at discrete wavelengths. Depending on the tool used tomeasure reflected signal, the available spectral range may be betweendeep ultraviolet to far infrared. As an example, the available spectralrange may be between about 100 nm to about 10,000 nm. The reflectedsignal may be obtained at various times over the course of an etchprocess. As examples, the reflected signal may be obtained at time stepsof duration between about 0.01 s and 10 s, and the number of such timesteps in an etch process may be between about 2 and 1000. In otherwords, in some examples, about 2 to 1000 measurements are conducted overthe course of an etch process.

In general, the optical signals may be obtained from any radiationscattered from a substrate surface. Scattered radiation refersgenerically to photons or beams which hit a physical object and thenkeep propagating in some direction. The scattered radiation may bereflected and/or refracted. Sometimes, the incident radiation isdiffracted, which occurs when the radiation incident on a substratesurface scatters at multiple angles. Examples include rough surfacescatter, in which the scattered radiation is diffuse (going off inmultiple directions—i.e., spread out relative to the incident beam) andscatter from periodic surfaces, in which case the scattered radiation isseparated into discrete scattered orders, each of which goes in adistinct direction. In some applications, the radiation is scattered indiffracted orders whose reflectance can be measured to determineendpoint. Of course, the disclosed methods and apparatus also apply toscattering from isolated structures that are not periodic.

Examples of metrology tools that may be used to measure optical signalsused with the invention include spectral reflectometers, ellipsometers,and scatterometers. Vendors of such tools include KLA-Tencor of SanJose, Calif. and Nanometrics of Milpitas, Calif. Scatterometry refers totools such as reflectometers and ellipsometers that are intended tomeasure properties of structures that are often periodic and thatreflect in the discrete diffracted orders.

Characteristics of a Model Used to Monitor Etch Geometry Progression

Independent variables are inputs to a model. Some or all of them aremeasured optical signal from light that interacts with a substrateundergoing etch or that has been etched. The interacting light may bereflected, refracted, scattered diffusely, diffracted, etc. and may beobtained by a metrology tool such as an in situ metrology tool. Theindependent variable(s) may be a property of the interacting light suchas reflected light intensity at one or more angles, etc. The measuredoptical signal(s) may be measured as a function of time, wavelength(frequency), polarization, or any combination of these. The measuredoptical signal(s) may be used in raw form or it may be modified (e.g.,filtered, normalized, vectorized, etc.) prior to being provided to themodel. An independent variable may represent an input and/or cause,and/or is tested to see if it is the cause. An independent variable mayalso be known as a “predictor variable,” “regressor,” “controlledvariable,” “manipulated variable,” “explanatory variable,” or “inputvariable.”

Dependent variables are output by the model. They may be calculatedvalues of one or more etch geometry parameters such as etch depth,pitch, and critical dimension. These geometric parameters may beprovided as a function of time, progressing over the course of an etchprocess. In some cases, an evolving geometric parameter such as etchdepth is repeatedly calculated using optical signals (independentvariables) and compared against an endpoint value, and when the value ofthe geometric parameter matches the endpoint value, the etch process isautomatically changed (e.g., concluded) and/or a notification isgenerated. The value of a dependent variable output by the model, andparticularly the value applied to, or used in a process endpointalgorithm which initiates the process control change, may be referred toas a “call” of the endpoint or other process state based on thedependent variable. Dependent variables are sometimes referred to asresponse variables.

The model relates dependent variable(s) to the independent variable(s).It does so using any one or many different forms. Examples includelinear combinations (e.g., a summation of weighted contributions of theindependent variables), non-linear expressions (e.g., second or higherorder polynomial expressions including the independent variables), lookup tables, classification trees, dynamic time warping, similarity metricdriven algorithms, pattern matching and classification, variations ofmultivariate statistics (PCA, PLS), and a host of novelty detectionalgorithms used in fault detection and classification schemes. In someexamples, the model is a neural network.

The model may have one or more of the features described in thefollowing.

In some implementations, the model is computationally efficient so thatit can process in situ optical signals in real time to generate ageometric etch parameter from the in situ optical information (e.g.,real time end point monitoring). In certain embodiments, the featurecharacterization algorithm (e.g., endpoint assessment) completesprocessing in about 100 ms or less (from the time it receives inputvariable values such as optical measurements). In certain embodiments,the feature characterization algorithm completes processing in about 20ms or less. Such rapid processing may be employed, for example, inapplications with critical step change requirements or in high etch rateprocesses (e.g., etch processes that complete in less than about aminute). In processes with many variations induced by the processingregime (such as in RF pulsing or gas pulsing) or when the waferstructure itself has a complicated structure (such as in stacks ofalternating materials), data arrays (e.g., thousands of them) may berequired, sometimes for each of multiple time samples (e.g., one hundredor more, or one thousand or more). The model's execution time alsodepends on the type of algorithm used. In some implementations the modelprocesses all or much of the time evolution of the spectral informationfrom the beginning of the etch process to the current time. This mayrequire large number of models being created such as with multiwayprincipal component analysis (PCA) and multiway partial least squares(PLS), where each model compares the optical measurement trajectoriesfrom the beginning of the etch until the current time step with respectto historical trajectories of corresponding time intervals. Such modelshave increased computational requirements both during model calibrationand during real-time process monitoring as the etch time gets longer. Insuch cases, the system may be configured with additional processingcapabilities such as processors with large amounts of buffer space,multithreading, and/or multiple cores.

In some implementations, a model call (output of a geometric parametersuch as an etch depth corresponding to an etch endpoint) is providedwith a “confidence.” The call may be given a low confidence if the modelpredicts a geometry outside the range of geometries used to generate orvalidate the model. For example, if the model determines that a featurebeing etched has a critical dimension that is narrower than that of anygeometries used to generate the model, a called etch depth end point maybe given a low confidence. Additionally, a call may be given a lowconfidence if the optical signals used as inputs are outside an expectedrange. In certain types of etch process, the signal variations fromnon-modeled factors influence the fit of the model and can reduceconfidence. Examples of such signal variations include “noise” fromillumination variations (lamp noise or laser noise), variations inhardware setup relative to those assumed in the model, etc. Inprobabilistic models, the confidence in a call may include acontribution from data used to develop such models (e.g., the amount ofsuch data and variations in it).

In certain embodiments, the model uses an optical output signal overonly a limited range of wavelengths (or other aspect of the opticalsignal), which may be selected for determining the geometric parameterof interest. The signal in this range is used as an independent variable(or a group of independent variables) for the model. In some suchimplementations, much of the available optical signal is not used as aninput. The selected range may represent a small fraction (e.g., lessthan about 10% or even a discrete value) of the full range of valuesthat can be measured by the metrology tool. Using a selected range as amodel input can require less computation, and therefore fastercalculation, to determine an etch feature's geometry. It can also allowselected dependent variables to be calculated without interference fromcorrelated geometric parameters; for example, etch depth can becalculated without significant interference from input signals thatstrongly correlate with critical dimension. For example, a firstwavelength range may strongly correlate with etch depth, while adifferent wavelength range may strongly correlate with criticaldimension but only weakly correlate with etch depth. A process focusingon etch depth may, to avoid obscuring signal, use only optical signalsin the first wavelength range.

Depending upon the optical tool used, the usable output signal may beconstrained to a narrow range of a characteristic other than wavelength.For example, the used output signal may be limited to a specificpolarization state, or to a specific direction with respect to thesubstrate and/or the incident light. This direction is sometimes thespecular direction (reflecting off the surface at the same angle inwhich it was incident, sometimes called zero-th order reflectance), butin the case of a diffracting periodic surface, the direction may be thatof a discrete order reflected at other than the specular direction;these are sometimes referred to as higher diffracted orders. Any one ormore directions associated with diffraction orders, including thezeroth-order reflected radiation, may be used.

In some examples, the selected wavelength range or other selectedoptical parameter range varies as a function of time during the etchprocess. In other words, the selected range or ranges of opticalparameters varies from one time increment to another. This may providean appropriate way to attack a problem when the spectral structure ofthe optical signal of interest varies from one time step to the next.For example, the center of a reflected intensity peak associated etchdepth may change in wavelength over the period of an etch process.

FIG. 2 presents an example of how an optical parameter (e.g., reflectedsignal intensity at particular direction) can vary with a feature ofinterest (in this case etch depth) and consequently vary with etch time.The panels of FIG. 2 show three reflectance intensity versus wavelengthspectra, each associated with a different etch depth and henceassociated with a different time. The top panel of FIG. 2 shows aspectrum at the beginning of an etch process, e.g., when a patternedmask is present but no etching has occurred. At this stage, thereflected intensity has a maximum at lambda1. The middle and lowerpanels show how the spectrum evolves over the course of the etchprocess. Notably the intensity peak shifts to longer wavelengths,lambda2 and lambda3 in this example.

In certain embodiments, the selected wavelength range or other selectedcharacteristic of the optical signal is chosen to increase (e.g.,maximize) the “targeted sensitivity” where changes in the geometricparameter of interest (e.g., etch depth) cause significant changes inthe measured optical parameter (e.g., reflectance intensity), butchanges in one or more other geometric parameters (e.g., criticaldimension) do not cause a significant change in the measured opticalparameter. This may be understood by the example of a time varyingmeasured optical signal that is a function of two or more correlatedgeometric parameters. A differential equation representing thissituation may present the derivative of the optical signal with respectto time as a function of the sum of terms, each including a derivativeof the optical signal with respect to one of the geometric parameters.dR/dt=(dR/dDepth)(dDepth/dt)+(dR/dCD)(dCD/dt).

In some implementations, the selected wavelengths or other opticalparameters are chosen to have a large value of dR/dDepth and a smallvalue of dR/dCD. This allows the model to calculate etch depth withoutsignificant contribution by (and interference from) signals that varywith changes in critical dimension. Of course, the selected opticalparameter range can be chosen to emphasize any selected featureparameter (e.g., pitch, etch angle, critical dimension, etc.). Further,the selected range of wavelengths (that well represent variations in thefeature of interest) may change over time.

In some implementations, multiple optical properties are measuredsimultaneously, thereby allowing resolution of multiple geometric etchparameters simultaneously. For example, both the intensity andpolarization (s- and p-polarization components) of the reflected signalmay be measured and provided to a model that employs them as separateindependent variables and calculates both time varying etch depth andcritical dimension. Other optical properties that can be measured wereidentified elsewhere herein. One example is the direction of thereflected radiation.

While most examples presented herein consider etch depth as thegeometric parameter of interest and critical dimension as a potentiallyobscuring geometric parameter, some applications may use geometricparameters differently. For example, critical dimension, pitch, sidewallangle, etc. may be the geometric parameter of interest. The presentdisclosure should be read with this understanding.

FIG. 3 presents a process for monitoring an etch process and makingadjustments if necessary. The depicted process has four phases: andinitial set up phase as illustrated in blocks 301 and 303, an etchprocess initiation phase as illustrated in block 305, an etch monitoringand adjustment phase as illustrated by the loop represented in blocks307, 309, 311, 313, and 317, and finally an etch conclusion phase asillustrated by block 315.

Initially, during the setup phase, the metrology tool and/or processorsacting on metrology data are set to provide metrology data appropriatefor a monitoring model to monitor an etch process. Thus, in theillustrated example, a process operation 301 sets the metrology tooland/or processors to capture or process only wavelengths or otheroptical parameters within a range appropriate for the monitoring. Asexplained above, an etch monitoring algorithm may rely on particularwavelengths or other optical parameters, which are a subset of allavailable optical parameters, for measurement and processing. Forexample, a narrow range of wavelengths in the visible or ultravioletportions of the spectrum may be suitable for real-time monitoring asubstrate's etch depth, critical dimension, or other geometricparameter.

In addition to an initial set of wavelengths or other optical parametersfor capture, the monitoring algorithm may need to employ variations insuch optical parameters over the course of the etch process. To thisend, the illustrated process includes an operation 303 which sets themetrology tool and/or processors to vary the captured and/or processedwavelengths or other optical parameters as the etch process evolves. Asan example, the initial range of wavelengths as set in operation 301 maystraddle the visible and ultraviolet portion of the electromagneticspectrum, but over time as the etch progresses, the range of wavelengthsshifts to entirely within the visible range. Such shift may be pre-setin operation 303.

It should be understood that either or both of set up operations 301 and303 are optional, and some etch monitoring processes do not requirethem. As an example, such processes may capture only a narrow band ofwavelengths, which are appropriate for the entire etch process. In otherexamples, the monitoring model may be designed or configured to operateon a wide range of wavelengths (or other optical parameter) in real timeand with high precision for the geometric parameter of interest.

Set up operations such as those of operations 301 and 303 may beimplemented in various ways. For example, optical settings on ametrology tool and/or data collection settings in processing systems areadjusted or bounded for purposes of the set up and/or controloperations.

After the initial setup phase is complete, the process initiates theetch process in an etch chamber as indicated by process operation 305.As understood by those of skill in the art, this may involve positioninga substrate in an etch chamber, evacuating the etch chamber, flowingprocess gas into the etch chamber, striking a plasma, and the like.Initially, in the etch process, the substrate may include only a mask orother structure for defining an etch pattern. The underlying material tobe etched has not been etched in any substantial way before the etchprocess is initiated in operation 305.

As the etch process unfolds, it is monitored in real time using anoptical signal from the substrate as collected by one or more metrologytools and processed according to the settings to find in operations 301and 303. See process block 307, which represent the continuingmeasurement of real-time optical signal from the substrate. Whilemonitoring the etch process, the etch/metrology system provides aportion of the optical signal (the set of wavelengths or other opticalparameter in the current range), appropriate for the current time step,and the model uses these signals for predicting the etch geometryparameter of interest. See process block 309. As explained, the modelmay be optimized to process only a particular range of opticalparameters (independent variables) at any given time step during theetch process. Operation 309 ensures that the model receives thecollected parameters, as appropriate, for the current time step.

Next, for the current time step, the model executes using the currentlyinput optical parameters and provides a predicted etch geometryparameter. This is illustrated at block 311. While the model iscalculating geometric parameters in real time, the monitoring algorithmchecks those parameters to determine whether they are within an expectedrange (for the current time step) or whether they signal an endpoint ofthe etch process. This check is illustrated at decision block 313.Assuming that the etch geometry parameter(s) predicted by the modelcontinues to fall within the expected range, the monitoring processcontinues to determine whether the current time increment requiresadjusting optical parameters for capture according to pre-existingsettings (e.g., settings defined at blocks 301 and/or 303). See processblock 317. Whether or not the current optical parameters for capture andprocessing are adjusted, process control loops back to block 307, wherethe metrology system continues to collect real-time optical signals. Asdescribed above, while this occurs, the processor and associatealgorithm continue to (i) provide the appropriate optical signal for thecurrent time step to the model (process block 309) and (ii) execute themodel to provide the predicted geometry etch parameter for the currenttime step (process block 311). Additionally, the processors andalgorithm continue to determine whether the predicted etch geometryparameter is within the expected range at process block 313.

At some point, the evaluation conducted in decision operation 313results in a negative finding, i.e., the etch parameter is outside theexpected range for the current time or the etch parameter has reached anendpoint. At that time, process flow is directed to a process operation315, which modifies or ends the current etch process, or sends anotification to an etch system which can effect automatic or manualintervention in the etch process. Such intervention may involve furtherevaluation to determine whether a course adjustment is required and/orwhether the process should be terminated.

Generating a Model that Calculates Time-Dependent Etch Geometry fromMeasured Optical Parameters

The model may be generated using a training set containing many datapoints, each having (i) one or more etch geometry values, and (ii) oneor more associated optical signal values that are predicted to be (orare) generated from a metrology tool probing a substrate having the etchgeometries. The one or more etch geometry feature values can impact theoptical readings from the metrology tool. Examples include etch depth,critical dimension, and other features discussed above. Examples of theoptical readings include reflectance spectra as a function of time.

The training set data points (geometric etch parameters and associatedoptical signal values) may be generated experimentally orcomputationally. In some embodiments, the etch parameters are generatedcomputationally using an etch profile model such as a Surface KineticModel (SKM). Such models are described below and in U.S. patentapplication Ser. No. 14/972,969, filed Dec. 17, 2015, which isincorporated herein by reference in its entirety. When using an SKM orother etch profile model to generate the etch geometry parameter values,the optical parameters generated from the geometry may be modeled orpredicted using an optical modeling routine such as the Rigorous CoupledWave Analysis (RCWA) method or similar technique.

RCWA is but one method that can be used to describe the characteristicsof reflected (diffracted, scattered) radiation from a periodic structuresuch as a grating, or transmitted radiation through such a grating. RCWAwas largely developed by Moharam and Gaylord and described in thescientific literature. See e.g., M. G. Moharam and T. K. Gaylord“Rigorous coupled-wave analysis of planar-grating diffraction” J. OptSoc of America, Vol. 71, Issue 7, pp. 811-818 (1981). RCWA calculatesthe intensity and polarization characteristics of the various diffractedorders (zeroth order and higher orders). Other optical modelling methodsthat can provide results include, but are not limited to, C method,Modal method, Rayleigh approximation, EFIE (e-field integrationequation), and Cf-FFT (conjugate gradient—fast fourier transform).

Rigorous coupled-wave analysis (RCWA) is a semi-analytical method incomputational electromagnetics that is often employed to solvescattering from periodic dielectric structures. It is a Fourier-spacemethod so devices and fields are represented as a sum of spatialharmonics. The method is based on Floquet's theorem that the solutionsof periodic differential equations can be expanded with Floquetfunctions (or sometimes referred as Block wave, especially insolid-state physics). A device is divided into layers that are eachuniform in the z direction. A staircase approximation is needed forcurved devices with properties such as dielectric permittivity gradedalong the z-direction. The electromagnetic modes in each layer arecalculated and analytically propagated through the layers. The overallproblem is solved by matching boundary conditions at each of theinterfaces between the layers using a technique like scatteringmatrices. To solve for the electromagnetic modes, which are decided bythe wave vector of the incident plane wave, in periodic dielectricmedium, Maxwell's equations (in partial differential form) as well asthe boundary conditions are expanded by the Floquet functions and turnedinto infinitely large algebraic equations. With the cutting off ofhigher order Floquet functions, depending on the accuracy andconvergence speed one needs, the infinitely large algebraic equationsbecome finite and thus solvable by computers.

From the training set, a regression model, neural network, or otherappropriate model for relating optical signal to etch geometry can begenerated. In one example, partial least squares is used to produce aregression model from the training set data. The resulting modelprovides a linear combination of multiple wavelength trajectories overtime to calculate etch geometry features of interest. As an example,form of the model may be represented as:

${{Etch}\mspace{14mu}{depth}} = {\sum\limits_{t = 0}^{t = T}\;{\sum\limits_{i}^{\underset{\underset{\underset{{slice}\mspace{11mu} t}{{at}\mspace{11mu}{times}}}{lamdas}}{selected}}{b_{t}\left( {a_{i}\mspace{11mu}{lamda}_{i}} \right)}_{t}}}$

Where lamba_(i) is the reflectance or other optical parameter at aselected wavelength and b_(t) and a_(i), are coefficients that vary withtime and wavelength, respectively.

In various embodiments, a model is generated using a selection processfor identifying limited ranges of wavelength or other characteristics ofthe optical signal to identify data that is a strong function of thegeometric parameter of interest and a weak function of one or moreother, potentially obscuring, geometric parameters. For example, theprocess will identify wavelengths of reflectance data that are sensitiveto changes in etch depth but relatively insensitive to changes incritical dimension.

Selecting ranges of optical signal values may be accomplished by varioustechniques such as principal component analysis (PCA) or partial leastsquares (PLS). PCA may be used as a data compression method which can beused to exclude wavelengths that do not contain significant variation inthe data set collected from a set of wafers or from a set of SKM modelsimulations. PLS can be used in combination with PCA where the principalcomponents obtained from the PCA model can be used as the X-block datafor the PLS model and correlation of with the Y-block data (geometricvariables) can be studied to select appropriate set of wavelengths.Alternatively, PLS can be used by itself to correlate raw reflectancedata as the X-block and geometric variables in the Y-block.

In an alternative approach, inspecting results of a surface kineticmodel or other accurate etch profile model over a wide range of opticalparameters is used to narrow down the selection of optical parameterrange(s). For example, an etch profile model such as a surface kineticmodel is used to identify expected etch geometry values for a given etchprocess, and these etch geometries are used as starting points tomanually and/or computationally vary, and from their variation toidentify optical parameter ranges that produce a relatively large changein optical signal due to changes in the target geometric parameter ofinterest and/or relatively small changes in optical signal due tochanges in one or more the non-target geometric parameters. Thus, onecan vary different geometric parameters and identify a range or rangesof wavelengths or other optical parameters that strongly vary as afunction of changes in one geometric parameter but not others. This canbe performed by calculating sensitivity matrices ofdR/dGeometricParameter for all parameters over large range ofwavelengths and down select a subset based on desired targetedsensitivity (e.g., wavelengths sensitive to depth but not sensitive toCD changes).

FIG. 4 presents a flow chart of a method for generating models inaccordance with certain embodiments. As shown in the depicted flow, theprocess begins at a block 403 where the model generating system receivesa target geometric parameter that is to be modeled as a function of theoptical signals produced using one or more optical metrology tools. Thegeometric parameter may be any of those identified above, e.g. the depthof an etched feature in a substrate. The choice of such parameter is, ofcourse, governed by the needs of the organization controlling theetching process and associated semiconductor device fabrication process.

A model is generated from a training set of data points, each providinga combination of the target geometric parameter value (e.g., an etchdepth) and one or more optical signal values produced in response to thegeometric parameter value. In other words, each member of a training setincludes a geometry value associated with a feature and associatedoptical signals produced from the particular feature. In someimplementations, a training set member may include multiple parametervalues for a given feature (e.g., etch depth and critical dimension).

To generate the model, the training set must be prepared experimentallyor computationally as indicated at process block 405. In certainembodiments, the process employs a training set of at least about 50members, or at least about 100 members, or at least about 200 members,or at least about 500 members. The training set members are usedcollectively to develop a relationship between the target geometricparameter and optical signals generated from such geometric parameter.

Alternatively, a single run (experimental or computational) thatproduces a set of profiles over multiple time steps can be used as thecenter-point of simulated design of experiments (DOE) to build a model.In this approach, a series of modifications are applied to the profilesby changing the geometric variables in a DOE fashion trying to capturethe effects of individual geometric variables and their cross terms onthe optical reflectance. Each modified profile is passed through opticalmodel (e.g., RCWA) calculation to obtain the corresponding opticalreflectance. The resulting set of optical reflectances and the geometricvariables can be used in PCA and/or PLS to down select a range ofwavelengths best correlates with the desired geometric variable.

In certain embodiments, before using the training set to generate themodel, the method identifies a subset of the optical signal values thatcorrelate strongly with the target geometric parameter and weakly withnon-target geometric parameters. See optional step 407 depicted in theprocess flow. As explained above, narrowing the range of optical signalsfor consideration may have various benefits such as providing morereliable determination of the target geometric parameter and/or doing sofaster by, e.g., consuming relatively little computational resource.

Assuming that the range of optical signal values is narrowed asillustrated in block 407, the process then optionally filters thetraining set to remove data outside the identified range of opticalsignal values. See block 409. In another approach, the model generationprocess simply generates additional data points for the training set,where such additional data points have optical signal values in therange identified in operation 407.

Regardless of whether the optional steps 407 and 409 are performed, theprocess ultimately uses the training set to generate a model relatingthe target geometric parameter values to optical signal values asindicated in a process block for 411. Various techniques for generatingthe model may be employed, such as those described above, includingneural networks and regression techniques including partial leastsquares.

Etch Profile Models Including Surface Kinetic Models

As mentioned above, an etch profile model, which relates etch geometryvalues to physical and/or chemical etch conditions, may be used forvarious purposes including generating data to produce a model used in anin situ metrology system for an etcher. In the context of an etchprofile model, an etch profile refers to any set of values for a set ofone or more geometric coordinates which may be used to characterize theshape of an etched feature on a semiconductor substrate. In a simplecase, an etch profile can be approximated as the width of a featuredetermined halfway to the base of the feature (the midpoint between thefeature's base (or bottom) and it's top opening on the surface of thesubstrate) as viewed through a 2-dimensional vertical cross-sectionalslice through the feature. In a more complicated example, an etchprofile may be series of feature widths determined at various elevationsabove the base of the feature as viewed through the same 2-dimensionalvertical cross-sectional slice.

As noted above, such a width may be referred to as a “criticaldimension” and the elevation from the base of the feature may bereferred to as the height or the z-coordinate of the so-referred-tocritical dimension. The etch profile may be represented in othergeometric references such as by a group of vectors from a common originor a stack of shapes such as trapezoids or triangles or a group ofcharacteristic shape parameters that define a typical etch profile suchas bow, straight or tapered sidewall, rounded bottom, facet etc.

In this way, a series of geometric coordinates (e.g., feature widths atdifferent elevations) maps out a discretized portrayal of a feature'sprofile. Note, that there are many ways to express a series ofcoordinates which represent feature width at different elevations. Forinstance, each coordinate might have a value which represents afractional deviation from some baseline feature width (such as anaverage feature width, or a vertically averaged feature width), or eachcoordinate might represent the change from the vertically adjacentcoordinate, etc. In any event, what is being referred to as “width” or“critical dimension” and, generally, the scheme being used for the setof profile coordinates used to represent an etch profile will be clearfrom the context and usage. The idea is that a set of coordinates areused represent the shape of the feature's etched profile. It is alsonoted that a series of geometric coordinates could also be used todescribe the full 3-dimensional shape of a feature's etched profile orother geometric characteristic, such as the shape of an etched cylinderor trench on a substrate surface. Thus, in some embodiments, a etchprofile model may provide a full 3-D etch shape of the feature beingmodeled.

Etch profile models compute an etch profile from a set of input etchreaction parameters (independent variables) characterizing theunderlying physical and chemical etch processes and reaction mechanisms.These processes are modelled as a function of time and location in agrid representing features being etched and their surroundings. Examplesof input parameters include plasma parameters such as ion flux andchemical reaction parameters such as the probability that a particularchemical reaction will occur. These parameters (and particularly, insome embodiments, the plasma parameters) may be obtained from varioussources, including other models that calculate them from general reactorconfigurations and process conditions such as pressure, substratetemperature, plasma source parameters (e.g., power, frequencies, dutycycles provided to the plasma source), reactants, and their flow rates.In some embodiments, such model is part of the etch profile model.

As explained, etch profile models take reaction parameters asindependent variables and generate etch profiles as response variables.In other words, a set of independent variables are the physical/chemicalprocess parameters used as inputs to the model, and response variablesare the etch profile features calculated by the model. The etch profilemodels employ one or more relationships between the reaction parametersand the etch profile. The relationships may include, e.g., coefficients,weightings, and/or other model parameters (as well as linear functionsof, second and higher order polynomial functions of, etc. the reactionparameters and/or other model parameters) that are applied to theindependent variables in a defined manner to generate the responsevariables, which are related to the etch profiles. Such weightings,coefficients, etc. may represent one or more of the reaction parametersdescribed above.

Some etch profile models employ independent variables that may becharacterized as fundamental reaction mechanistic parameters and may beviewed as fundamental to the underlying chemistry and physics andtherefore the experimental process engineer generally does not havecontrol over these quantities. In the etch profile model, thesevariables are applied at each location of a grid and at multiple times,separated by defined time steps. In some implementations, the gridresolution may vary between about a few Angstroms and about amicrometer. In some implementations, the time steps may vary betweenabout 1e-15 and 1e-10 seconds. In certain embodiments, the model employstwo types of mechanistic independent variables: (1) local plasmaparameters and (2) local chemical reaction parameters. These parametersare “local” in the sense that they may vary as a function of position,in some cases down to the resolution of the grid. Examples of the plasmaparameters include local plasma properties such as fluxes and energiesof particles such ions, radicals, photons, electrons, excited species,depositor species and their energy and angular distributions etc.Examples of chemical and physico-chemical reaction parameters includerate constants (e.g., probabilities that a particular chemical reactionwill occur at a particular time), sticking coefficients, energythreshold for etch, reference energy, exponent of energy to definesputter yields, angular yield functions and its parameters, etc.Further, the parameterized chemical reactions include reactions in whichthe reactants include the material being etched and an etchant. Itshould be understood that the chemical reaction parameters may includevarious types of reactions in addition to the reactions that directlyetch the substrate. Examples of such reactions include side reactions,including parasitic reactions, deposition reactions, reactions ofby-products, etc. Any of these might affect the overall etch rate. Itshould also be understood that the model may require other inputparameters, in addition to the above-mentioned plasma and chemicalreaction input parameters. Examples of such other parameters include thetemperature at the reaction sites, the partial pressure of reactants,etc. In some cases, these and/or other non-mechanistic parameters may beinput in a module that outputs some of the mechanistic parameters.

In some embodiments, values for the independent variables are obtainedfrom various sources such as the literature, calculations by othercomputational modules or models, etc. In some embodiments, theindependent variables—such as the plasma parameters—may be determined byusing a model such as, for the case of the plasma parameters, from anetch chamber plasma model. Such models may calculate the applicableinput etch profile model parameters from various process parameters overwhich the process engineer does have control (e.g., by turning aknob)—e.g., chamber environment parameters such as pressure, flow rate,plasma power, wafer temperature, ICP coil currents, bias voltages/power,pulsing frequency, pulse duty cycle, and the like.

When running an etch profile model, some of the independent variablesmay be set to known or expected parameter values used to perform theexperiments. For example, the plasma parameters may be fixed to known orexpected values at locations in modeled domain. Other independentvariables are those which are tuned. For example, the chemical reactionparameters may be the tuned. Thus, in a series of runs corresponding toa given measured experimental etch profile, the model parameters arevaried in order to elucidate how to choose values of these parameters tobest optimize the model. In other embodiments, the plasma and chemicalreaction parameters are known ahead of time.

Etch profile models may take any of many different forms. Ultimately,they provide a relationship between the independent and dependent (orresponse) variables. The relationship may be linear or nonlinear.Generally, an etch profile model is what is referred to in the art as acell-based Monte Carlo surface reaction model. These models, in theirvarious forms, operate to simulate a wafer feature's topographicalevolution over time in the context of semiconductor wafer fabrication.The models launch pseudo-particles with energy and angular distributionsproduced by a plasma model or experimental diagnostics for arbitraryradial locations on the wafer. The pseudo-particles are statisticallyweighted to represent the fluxes of radicals and ions to the surface.The models address various surface reaction mechanisms resulting inetching, sputtering, mixing, and deposition on the surface to predictprofile evolution. During a Monte Carlo integration, the trajectories ofvarious ion and neutral pseudo-particles are tracked within a waferfeature until they either react or leave the computational domain. Theetch profile model may be able to predict features of etching,stripping, atomic layer etching, ionized metal physical vapordeposition, and plasma enhanced chemical vapor deposition on variousmaterials. In some embodiments, an etch profile model utilizes arectilinear mesh in two or three dimensions, the mesh having a fineenough resolution to adequately address/model the dimensions of thewafer feature (although, in principle, the mesh (whether 2D or 3D) couldutilize non-rectilinear coordinates as well). The mesh may be viewed asan array of grid-points in two or three dimensions. It may also beviewed as an array of cells which represent the local area in 2D, orvolume in 3D, associated with (centered at) each grid-point. Each cellwithin the mesh may represent a different solid material or a mixture ofmaterials. Whether a 2D or 3D mesh is chosen as a basis for the modelingmay depend on the class/type of wafer feature being modelled. Forinstance, a 2D mesh may be used to model a long trench feature (e.g., ina polysilicon substrate), the 2D mesh delineating the trench'scross-sectional shape under the assumption that the geometry of the endsof the trench are not too relevant to the reactive processes takingplace down the majority of the trench's length away from its ends (i.e.,for purposes of this cross-sectional 2D model, the trench is assumedinfinite, again a reasonable assumption for a trench feature away fromits ends). On the other hand, it may be appropriate to model a circularvia feature (a through-silicon via (TSV)) using a 3D mesh (since the x,yhorizontal dimensions of the feature are on par with each other).

Mesh spacing may range from sub-nanometer (e.g., from 1 Angstrom) up toseveral micrometers (e.g., 10 micrometers). Generally, each mesh cell isassigned a material identity, for example, photoresists, polysilicon,plasma (e.g., in the spatial region not occupied by the feature), whichmay change during the profile evolution. Solid phase species arerepresented by the identity of the computational cell; gas phase speciesare represented by computational pseudo-particles. In this manner, themesh provides a reasonably detailed representation (e.g., forcomputational purposes) of the wafer feature and surrounding gasenvironment (e.g., plasma) as the geometry/topology of the wafer featureevolves over time in a reactive etch process.

To train and optimize the etch profile models presented in the previoussection, various experiments may be performed in order to determine—asaccurately as the experiments allow—the actual etch profiles whichresult from actual etch processes performed under the various processconditions as specified by various sets of etch process parameters.Thus, for instance, one specifies a first set of values for a set ofetch process parameters—such as etchant flow rate, plasma power,temperature, pressure, etc.—sets up the etch chamber apparatusaccordingly, flows etchant into the chamber, strikes the plasma, etc.,and proceeds with the etching of the first semiconductor substrate togenerate a first etch profile. One then specifies a second set of valuesfor the same set of etch process parameters, etches a second substrateto generate a second etch profile, and so forth.

Various combinations of process parameters may be used to present abroad or focused process space, as appropriate, to train the etchprofile model. The same combinations of process parameters are then usedto calculate (independent) input parameters, such as the mechanisticparameters, to the etch profile model to provide etch profile outputs(response variables) that can be compared against the experimentalresults. Because experimentation can be costly and time consuming,techniques can be employed to design experiments in a way that reducesthe number of experiments that need be conducted to provide a robusttraining set for optimizing the etch profile model. Techniques such asdesign of experiments (DOE) may be employed for this purpose. Generally,such techniques determine which sets of process parameters to use invarious experiments. They choose the combinations of process parametersby considering statistical interactions between process parameters,randomization, and the like. As an example, DOE may identify a smallnumber of experiments covering a limited range of parameters around thecenter point of a process that has been finalized.

Typically, a researcher will conduct all experiments early in the modeloptimization process and use only those experiments in the optimizationroutine iterations until convergence. Alternatively, an experimentdesigner may conduct some experiments for early iterations of theoptimization and additional experiments later as the optimizationproceeds. The optimization process may inform the experiment designer ofparticular parameters to be evaluated and hence particular experimentsto be run for later iterations.

One or more in-situ or offline metrology tools may be used to measurethe experimental etch profiles which result from these experimental etchprocess operations. Measurements made be made at the end of the etchprocesses, during the etch processes, or at one or more times during theetch processes. When measurements are made at the end of an etchprocess, the measurement methodology may be destructive, when made atintervals during the etch process, the measurement methodology wouldgenerally be non-destructive (so not to disrupt the etch). Examples ofappropriate metrology techniques include, but not limited to, LSR, OCD,and cross-sectional SEM. Note that a metrology tool may directly measurea feature's profile, such as is the case of SEM (wherein the experimentbasically images a feature's etch profile), or it may indirectlydetermine a feature's etch profile, such as in the case of OCDmeasurements (where some post-processing is done to back-out thefeature's etch profile from the actual measured data).

In any event, the result of the etch experiments and metrologyprocedures is a set of measured etch profiles, each generally includinga series of values for a series of coordinates or a set of grid valueswhich represent the shape of the feature's profile as described above.The etch profiles may then be used as inputs to train, optimize, andimprove the computerized etch profile models as described below.

Applications of Computerized Etch Profile Models

In certain embodiments, an etch profile model may be integrated with anetcher apparatus or into the infrastructure of a semiconductorfabrication facility which deploys one or more etcher apparatuses. Theetch profile model may be used to determine appropriate adjustments toprocess parameters to provide a desired etch profile or to understandthe effect of a change in process parameters on the etch profile. Thus,for instance, a system for processing semiconductor substrates within afabrication facility may include an etcher apparatus for etchingsemiconductor substrates whose operation is adjusted by a set ofindependent input parameters which are controlled by a controller whichimplements an etch profile model. As describe below, a suitablecontroller for controlling the operation of the etcher apparatustypically includes a processor and a memory, the memory storing the etchprofile model, and the processor using the stored etch profile model tocompute etched feature profiles for a given set of values of a set ofinput process parameters. After computing a profile, in someembodiments, the controller may (in response to the shape of thecomputed profile) adjust the operation of the etcher apparatus byvarying one or more values of the set of independent input parameters.

Generally, an etcher apparatus which may be used with the disclosed etchprofile models may be any sort of semiconductor processing apparatussuitable for etching semiconductor substrates by removing material fromtheir surface. In some embodiments, the etcher apparatus may constitutean inductively-coupled plasma (ICP) reactor; in some embodiments, it mayconstitute a capacitively-coupled plasma (CCP) reactor. Thus, an etcherapparatus for use with these disclosed etch profile models may have aprocessing chamber, a substrate holder for holding a substrate withinthe processing chamber, and a plasma generator for generating a plasmawithin the processing chamber. The apparatus may further include one ormore valve-controlled process gas inlets for flowing one or more processgases into the processing chamber, one or more gas outlets fluidicallyconnected to one or more vacuum pumps for evacuating gases from theprocessing chamber, etc. Further details concerning etcher apparatuses(also generally referred to as etch reactors, or plasma etch reactors,etc.) are provided below.

Capacitively Coupled Plasma (CCP) Reactors for Use in Etch Operations

Capacitively coupled plasma (CCP) reactors are described in U.S. Pat.No. 8,552,334, filed Feb. 9, 2009 as U.S. patent application Ser. No.12/367,754, and titled “ADJUSTABLE GAP CAPACITIVELY COUPLED RF PLASMAREACTOR INCLUDING LATERAL BELLOWS AND NON-CONTACT PARTICLE SEAL,” and inU.S. patent application Ser. No. 14/539,121, filed Nov. 12, 2014, andtitled “ADJUSTMENT OF VUV EMISSION OF A PLASMA VIA COLLISIONAL RESONANTENERGY TRANSFER TO AN ENERGY ABSORBER GAS,” each of which is herebyincorporated by reference in its entirety for all purposes. In certainembodiments, a capacitively coupled reactor executes substrate etchingusing an etch geometry model for end point detection or other control ormonitoring operation.

FIGS. 5A-5C illustrate an embodiment of an adjustable gap capacitivelycoupled confined RF plasma reactor 500. As depicted, a vacuum processingchamber 502 includes a chamber housing 504, surrounding an interiorspace housing a lower electrode 506. In an upper portion of the chamber502 an upper electrode 508 is vertically spaced apart from the lowerelectrode 506. Planar surfaces of the upper and lower electrodes 508,506 (configured to be used for plasma generation) are substantiallyparallel and orthogonal to the vertical direction between theelectrodes. In certain embodiments, the upper and lower electrodes 508,506 are circular and coaxial with respect to a vertical axis. A lowersurface of the upper electrode 508 faces an upper surface of the lowerelectrode 506. The spaced apart facing electrode surfaces define anadjustable gap 510 there between. During plasma generation, the lowerelectrode 506 is supplied RF power by an RF power supply (match) 520. RFpower is supplied to the lower electrode 506 though through an RF supplyconduit 522, an RF strap 524 and an RF power member 526. A groundingshield 536 may surround the RF power member 526 to provide a moreuniform RF field to the lower electrode 506. As described in U.S. Pat.Publication No. 2008/0171444 (which is hereby incorporated by referencein its entirety for all purposes), a wafer is inserted through waferport 582 and supported in the gap 510 on the lower electrode 506 forprocessing, a process gas is supplied to the gap 510 and excited intoplasma state by the RF power. The upper electrode 508 can be powered orgrounded.

In the embodiment shown in FIGS. 5A-5C, the lower electrode 506 issupported on a lower electrode support plate 516. An insulator ring 514interposed between the lower electrode 506 and the lower electrodesupport plate 516 insulates the lower electrode 506 from the supportplate 516. An RF bias housing 530 supports the lower electrode 506 on anRF bias housing bowl 532. The bowl 532 is connected through an openingin a chamber wall plate 518 to a conduit support plate 538 by an arm 534of the RF bias housing 530. In a preferred embodiment, the RF biashousing bowl 532 and RF bias housing arm 534 are integrally formed asone component, however, the arm 534 and bowl 532 can also be twoseparate components bolted or joined together.

The RF bias housing arm 534 includes one or more hollow passages forpassing RF power and facilities, such as gas coolant, liquid coolant, RFenergy, cables for lift pin control, electrical monitoring and actuatingsignals from outside the vacuum chamber 502 to inside the vacuum chamber502 at a space on the backside of the lower electrode 506. The RF supplyconduit 522 is insulated from the RF bias housing arm 534, the RF biashousing arm 534 providing a return path for RF power to the RF powersupply 520. A facilities conduit 540 provides a passageway for facilitycomponents. Further details of the facility components are described inU.S. Pat. No. 5,948,704 and U.S. Pat. Pub. No. 2008/0171444 (both ofwhich are hereby incorporated by reference in their entirety for allpurposes) and are not shown here for simplicity of description. The gap510 is preferably surrounded by a confinement ring assembly (not shown),details of which can be found in U.S. Pat. Pub. No. 2007/0284045 (whichis hereby incorporated by reference in its entirety for all purposes).

The conduit support plate 538 is attached to an actuation mechanism 542.Details of an actuation mechanism are described in U.S. Pat. Pub. No.2008/0171444 (which is hereby incorporated by reference in its entiretyfor all purposes). The actuation mechanism 542, such as a servomechanical motor, stepper motor or the like is attached to a verticallinear bearing 544, for example, by a screw gear 546 such as a ballscrew and motor for rotating the ball screw. During operation to adjustthe size of the gap 510, the actuation mechanism 542 travels along thevertical linear bearing 544. FIG. 5A illustrates the arrangement whenthe actuation mechanism 542 is at a high position on the linear bearing544 resulting in a small gap 510 a. FIG. 5B illustrates the arrangementwhen the actuation mechanism 542 is at a mid-position on the linearbearing 544. As shown, the lower electrode 506, the RF bias housing 530,the conduit support plate 538, the RF power supply 520 have all movedlower with respect to the chamber housing 504 and the upper electrode508, resulting in a medium size gap 510 b.

FIG. 5C illustrates a large gap 510 c when the actuation mechanism 542is at a low position on the linear bearing. Preferably, the upper andlower electrodes 508, 506 remain coaxial during the gap adjustment andthe facing surfaces of the upper and lower electrodes across the gapremain parallel.

This embodiment allows the gap 510 between the lower and upperelectrodes 506, 508 in the CCP chamber 502 during multi-step etchprocesses to be adjusted, for example, in order to maintain uniform etchacross a large diameter substrate such as 300 mm wafers or flat paneldisplays. In particular, this embodiment pertains to a mechanicalarrangement to facilitate the linear motion necessary to provide theadjustable gap between lower and upper electrodes 506, 508.

FIG. 5A illustrates laterally deflected bellows 550 sealed at aproximate end to the conduit support plate 538 and at a distal end to astepped flange 528 of chamber wall plate 518. The inner diameter of thestepped flange defines an opening 512 in the chamber wall plate 518through which the RF bias housing arm 534 passes. The laterallydeflected bellows 550 provides a vacuum seal while allowing verticalmovement of the RF bias housing 530, conduit support plate 538 andactuation mechanism 542. The RF bias housing 530, conduit support plate538 and actuation mechanism 542 can be referred to as a cantileverassembly. Preferably, the RF power supply 520 moves with the cantileverassembly and can be attached to the conduit support plate 538. FIG. 5Bshows the bellows 550 in a neutral position when the cantilever assemblyis at a mid-position. FIG. 5C shows the bellows 550 laterally deflectedwhen the cantilever assembly is at a low position.

A labyrinth seal 548 provides a particle barrier between the bellows 550and the interior of the plasma processing chamber housing 504. A fixedshield 556 is immovably attached to the inside inner wall of the chamberhousing 504 at the chamber wall plate 518 so as to provide a labyrinthgroove 560 (slot) in which a movable shield plate 558 moves verticallyto accommodate vertical movement of the cantilever assembly. The outerportion of the movable shield plate 558 remains in the slot at allvertical positions of the lower electrode 506.

In the embodiment shown, the labyrinth seal 548 includes a fixed shield556 attached to an inner surface of the chamber wall plate 518 at aperiphery of the opening 512 in the chamber wall plate 518 defining alabyrinth groove 560. The movable shield plate 558 is attached andextends radially from the RF bias housing arm 534 where the arm 534passes through the opening 512 in the chamber wall plate 518. Themovable shield plate 558 extends into the labyrinth groove 560 whilespaced apart from the fixed shield 556 by a first gap and spaced apartfrom the interior surface of the chamber wall plate 518 by a second gapallowing the cantilevered assembly to move vertically. The labyrinthseal 548 blocks migration of particles spalled from the bellows 550 fromentering the vacuum chamber interior and blocks radicals from processgas plasma from migrating to the bellows 550 where the radicals can formdeposits which are subsequently spalled.

FIG. 5A shows the movable shield plate 558 at a higher position in thelabyrinth groove 560 above the RF bias housing arm 534 when thecantilevered assembly is in a high position (small gap 510 a). FIG. 5Cshows the movable shield plate 558 at a lower position in the labyrinthgroove 560 above the RF bias housing arm 534 when the cantileveredassembly is in a low position (large gap 510 c). FIG. 5B shows themovable shield plate 558 in a neutral or mid position within thelabyrinth groove 560 when the cantilevered assembly is in a mid position(medium gap 510 b). While the labyrinth seal 548 is shown as symmetricalabout the RF bias housing arm 534, in other embodiments the labyrinthseal 548 may be asymmetrical about the RF bias arm 534.

Inductively Coupled Plasma Reactors for Use in Etch Operations

Inductively coupled plasma (ICP) reactors are described in US Pat. Pub.No. 2014/0170853, filed Dec. 10, 2013, and titled “IMAGE REVERSAL WITHAHM GAP FILL FOR MULTIPLE PATTERNING,” and in U.S. patent applicationSer. No. 14/539,121, filed Nov. 12, 2014, and titled “ADJUSTMENT OF VUVEMISSION OF A PLASMA VIA COLLISIONAL RESONANT ENERGY TRANSFER TO ANENERGY ABSORBER GAS,” each of which is hereby incorporated by referencein its entirety for all purposes.

For instance, FIG. 6 schematically shows a cross-sectional view of aninductively coupled plasma etching apparatus 600 appropriate forimplementing certain embodiments herein, an example of which is a Kiyo™reactor, produced by Lam Research Corp. of Fremont, Calif. Theinductively coupled plasma etching apparatus 600 includes an overalletching chamber structurally defined by chamber walls and a window 611.The chamber walls may be fabricated from stainless steel or aluminum.The window 611 may be fabricated from quartz or other dielectricmaterial. An optional internal plasma grid 651 divides the overalletching chamber into an upper sub-chamber 602 and a lower sub-chamber603. In most embodiments, plasma grid 651 may be removed, therebyutilizing a chamber space made of sub-chambers 602 and 603. A chuck 617is positioned within the lower sub-chamber 603 near the bottom innersurface. The chuck 617 is configured to receive and hold a semiconductorwafer 619 upon which the etching process is performed. The chuck 617 canbe an electrostatic chuck for supporting the wafer 619 when present. Insome embodiments, an edge ring (not shown) surrounds chuck 617, and hasan upper surface that is approximately planar with a top surface of awafer 619, when present over chuck 617. The chuck 617 also includeselectrostatic electrodes for chucking and dechucking the wafer. A filterand DC clamp power supply (not shown) may be provided for this purpose.Other control systems for lifting the wafer 619 off the chuck 617 canalso be provided. The chuck 617 can be electrically charged using an RFpower supply 623. The RF power supply 623 is connected to matchingcircuitry 621 through a connection 627. The matching circuitry 621 isconnected to the chuck 617 through a connection 625. In this manner, theRF power supply 623 is connected to the chuck 617.

Elements for plasma generation include a coil 633 is positioned abovewindow 611. The coil 633 is fabricated from an electrically conductivematerial and includes at least one complete turn. The example of a coil633 shown in FIG. 6 includes three turns. The cross-sections of coil 633are shown with symbols, and coils having an “X” extend rotationally intothe page, while coils having a “•” extend rotationally out of the page.Elements for plasma generation also include an RF power supply 641configured to supply RF power to the coil 633. In general, the RF powersupply 641 is connected to matching circuitry 639 through a connection645. The matching circuitry 639 is connected to the coil 633 through aconnection 643. In this manner, the RF power supply 641 is connected tothe coil 633. An optional Faraday shield 649 is positioned between thecoil 633 and the window 611. The Faraday shield 649 is maintained in aspaced apart relationship relative to the coil 633. The Faraday shield649 is disposed immediately above the window 611. The coil 633, theFaraday shield 649, and the window 611 are each configured to besubstantially parallel to one another. The Faraday shield 649 mayprevent metal or other species from depositing on the dielectric windowof the plasma chamber.

Process gases (e.g. helium, neon, etchant, etc.) may be flowed into theprocessing chamber through one or more main gas flow inlets 660positioned in the upper chamber and/or through one or more side gas flowinlets 670. Likewise, though not explicitly shown, similar gas flowinlets may be used to supply process gases to the capacitively coupledplasma processing chamber shown in FIGS. 5A-5C. A vacuum pump, e.g., aone or two stage mechanical dry pump and/or turbomolecular pump 640, maybe used to draw process gases out of the process chamber 624 and tomaintain a pressure within the process chamber 601. A valve-controlledconduit may be used to fluidically connect the vacuum pump to theprocessing chamber so as to selectively control application of thevacuum environment provided by the vacuum pump. This may be doneemploying a closed-loop-controlled flow restriction device, such as athrottle valve (not shown) or a pendulum valve (not shown), duringoperational plasma processing. Likewise, a vacuum pump and valvecontrolled fluidic connection to the capacitively coupled plasmaprocessing chamber in FIGS. 5A-5C may also be employed.

During operation of the apparatus, one or more process gases may besupplied through the gas flow inlets 660 and/or 670. In certainembodiments, process gas may be supplied only through the main gas flowinlet 660, or only through the side gas flow inlet 670. In some cases,the gas flow inlets shown in the figure may be replaced more complex gasflow inlets, one or more showerheads, for example. The Faraday shield649 and/or optional grid 651 may include internal channels and holesthat allow delivery of process gases to the chamber. Either or both ofFaraday shield 649 and optional grid 651 may serve as a showerhead fordelivery of process gases.

Radio frequency power is supplied from the RF power supply 641 to thecoil 633 to cause an RF current to flow through the coil 633. The RFcurrent flowing through the coil 633 generates an electromagnetic fieldabout the coil 633. The electromagnetic field generates an inductivecurrent within the upper sub-chamber 602. The physical and chemicalinteractions of various generated ions and radicals with the wafer 619selectively etch features of the wafer.

If the plasma grid is used such that there is both an upper sub-chamber602 and a lower sub-chamber 603, the inductive current acts on the gaspresent in the upper sub-chamber 602 to generate an electron-ion plasmain the upper sub-chamber 602. The optional internal plasma grid 651limits the amount of hot electrons in the lower sub-chamber 603. In someembodiments, the apparatus is designed and operated such that the plasmapresent in the lower sub-chamber 603 is an ion-ion plasma.

Both the upper electron-ion plasma and the lower ion-ion plasma maycontain positive and negative ions, though the ion-ion plasma will havea greater ratio of negative ions to positive ions. Volatile etchingbyproducts may be removed from the lower-subchamber 603 through port622.

The chuck 617 disclosed herein may operate at elevated temperaturesranging between about 10° C. and about 250° C. The temperature willdepend on the etching process operation and specific recipe. In someembodiments, the chamber 601 may also operate at pressures in the rangeof between about 1 mTorr and about 95 mTorr. In certain embodiments, thepressure may be higher as disclosed above.

Chamber 601 may be coupled to facilities (not shown) when installed in aclean room or a fabrication facility. Facilities include plumbing thatprovide processing gases, vacuum, temperature control, and environmentalparticle control. These facilities are coupled to chamber 601, wheninstalled in the target fabrication facility. Additionally, chamber 601may be coupled to a transfer chamber that allows robotics to transfersemiconductor wafers into and out of chamber 601 using typicalautomation.

Also shown in FIG. 6 is system controller 650. As described furtherbelow, such a system controller 650 may control some or all of theoperations of an etcher apparatus, not limited to chamber 601, includingadjustment of the etcher's operation in response to the generation of acomputed etch geometry (e.g., feature depth or critical dimension) usinga model as described herein.

System Controllers

A system controller may be used to control etching operations (or otherprocessing operations) in any of the above described processingapparatuses, such as the CCP etcher apparatuses shown in FIGS. 5A-5C,and/or the ICP etcher apparatus shown in FIG. 6. In particular, thesystem controller may implement a etch geometry model as described aboveand adjust operation of an etcher apparatus in response to computed etchprofiles generated using the etch geometry model (as described above).

An example of a system controller in communication with an etcherapparatus is schematically illustrated in FIG. 6. As shown in FIG. 6,system controller 650 includes one or more memory devices 656, one ormore mass storage devices 654, and one or more processors 652. Processor652 may include one or more CPUs, ASICs, general-purpose computer(s)and/or specific purpose computer(s), one or more analog and/or digitalinput/output connection(s), one or more stepper motor controllerboard(s), etc.

In some embodiments, a system controller (e.g., 650 in FIG. 6) controlssome or all of the operations of a process tool (e.g., etcher apparatus600 in FIG. 6) including the operations of its individual processstations. Machine-readable system control instructions 658 may beprovided for implementing/performing the film deposition and/or etchprocesses described herein. The instructions may be provided onmachine-readable, non-transitory media which may be coupled to and/orread by the system controller. The instructions may be executed onprocessor 652—the system control instructions, in some embodiments,loaded into memory device 656 from mass storage device 654. Systemcontrol instructions may include instructions for controlling thetiming, mixture of gaseous and liquid reactants, chamber and/or stationpressures, chamber and/or station temperatures, wafer temperatures,target power levels, RF power levels (e.g., DC power levels, RF biaspower levels), RF exposure times, substrate pedestal, chuck, and/orsusceptor positions, and other parameters of a particular processperformed by a process tool.

Semiconductor substrate processing operations may employ various typesof processes including, but not limited to, processes related to theetching of film on substrates (including atomic layer etch (ALE)operations involving plasma-activation of surface adsorbed etchants,see, e.g., U.S. patent application Ser. No. 14/539,121, filed Nov. 12,2014, and titled “ADJUSTMENT OF VUV EMISSION OF A PLASMA VIA COLLISIONALRESONANT ENERGY TRANSFER TO AN ENERGY ABSORBER GAS,” which is herebyincorporated by reference in its entirety for all purposes), depositionprocesses (such as atomic layer deposition (ALD), by plasma-activationof surface adsorbed film precursors), as well as other types ofsubstrate processing operations.

Thus, for example, with respect to a processing apparatus for performingplasma-based etch processes, the machine-readable instructions executedby a system controller may include instructions for generating acomputed etch profile from an optimized etch profile model and adjustingoperation of the plasma generator in response to the computed etchprofile.

System control instructions 658 may be configured in any suitable way.For example, various process tool component subroutines or controlobjects may be written to control operation of the process toolcomponents necessary to carry out various process tool processes. Systemcontrol instructions may be coded in any suitable computer readableprogramming language. In some embodiments, system control instructionsare implemented in software, in other embodiments, the instructions maybe implemented in hardware—for example, hard-coded as logic in an ASIC(application specific integrated circuit), or, in other embodiments,implemented as a combination of software and hardware.

In some embodiments, system control software 658 may includeinput/output control (IOC) sequencing instructions for controlling thevarious parameters described above. For example, each phase of adeposition and/or etch process or processes may include one or moreinstructions for execution by the system controller. The instructionsfor setting process conditions for a film deposition and/or etch processphase, for example, may be included in a corresponding deposition and/oretch recipe phase. In some embodiments, the recipe phases may besequentially arranged, so that all instructions for a process phase areexecuted concurrently with that process phase.

Other computer-readable instructions and/or programs stored on massstorage device 654 and/or memory device 656 associated with systemcontroller 650 may be employed in some embodiments. Examples of programsor sections of programs include a substrate positioning program, aprocess gas control program, a pressure control program, a heatercontrol program, and a plasma control program.

A substrate positioning program may include instructions for processtool components that are used to load the substrate onto pedestal and tocontrol the spacing between the substrate and other parts of processtool. The positioning program may include instructions for appropriatelymoving substrates in and out of the reaction chamber as necessary todeposit and/or etch film on the substrates.

A process gas control program may include instructions for controllinggas composition and flow rates and optionally for flowing gas into thevolumes surrounding one or more process stations prior to depositionand/or etch in order to stabilize the pressure in these volumes. In someembodiments, the process gas control program may include instructionsfor introducing certain gases into the volume(s) surrounding the one ormore process stations within a processing chamber during film depositionand/or etching operations on substrates. The process gas control programmay also include instructions to deliver these gases at the same rates,for the same durations, or at different rates and/or for differentdurations depending on the composition of the film being depositedand/or the nature of the etching process involved. The process gascontrol program may also include instructions for atomizing/vaporizing aliquid reactant in the presence of helium or some other carrier gas in aheated injection module.

A pressure control program may include instructions for controlling thepressure in the process station by regulating, for example, a throttlevalve in the exhaust system of the process station, a gas flow into theprocess station, etc. The pressure control program may includeinstructions for maintaining the same or different pressures duringdeposition of the various film types on the substrates and/or etching ofthe substrates.

A heater control program may include instructions for controlling thecurrent to a heating unit that is used to heat the substrates.Alternatively or in addition, the heater control program may controldelivery of a heat transfer gas (such as helium) to the substrate. Theheater control program may include instructions for maintaining the sameor different temperatures in the reaction chamber and/or volumessurrounding the process stations during deposition of the various filmtypes on the substrates and/or etching of the substrates.

A plasma control program may include instructions for setting RF powerlevels, frequencies, and exposure times in one or more process stationsin accordance with the embodiments herein. In some embodiments, theplasma control program may include instructions for using the same ordifferent RF power levels and/or frequencies and/or exposure timesduring film deposition on and/or etching of the substrates.

In some embodiments, there may be a user interface associated with thesystem controller. The user interface may include a display screen,graphical software displays of the apparatus and/or process conditions,and user input devices such as pointing devices, keyboards, touchscreens, microphones, etc.

In some embodiments, parameters adjusted by system controller may relateto process conditions. Non-limiting examples include process gascompositions and flow rates, temperatures (e.g., substrate holder andshowerhead temperatures), pressures, plasma conditions (such as RF biaspower levels and exposure times), etc. These parameters may be providedto the user in the form of a recipe, which may be entered utilizing theuser interface.

Signals for monitoring the processes may be provided by analog and/ordigital input connections of the system controller from various processtool sensors. The signals for controlling the processes may be output onthe analog and/or digital output connections of the process tool.Non-limiting examples of process tool sensors that may be monitoredinclude mass flow controllers (MFCs), pressure sensors (such asmanometers), temperature sensors such as thermocouples, etc.Appropriately programmed feedback and control algorithms may be usedwith data from these sensors to maintain process conditions.

The various apparatuses and methods described above may be used inconjunction with lithographic patterning tools and/or processes, forexample, for the fabrication or manufacture of semiconductor devices,displays, LEDs, photovoltaic panels and the like. Typically, though notnecessarily, such tools will be used or processes conducted togetherand/or contemporaneously in a common fabrication facility.

In some implementations, a controller is part of a system, which may bepart of the above-described etchers. Such systems can comprisesemiconductor processing equipment, including a processing tool ortools, chamber or chambers, a platform or platforms for processing,and/or specific processing components (a wafer pedestal, a gas flowsystem, etc.). These systems may be integrated with electronics forcontrolling their operation before, during, and after processing of asemiconductor wafer or substrate. The electronics may be referred to asthe “controller,” which may control various components or subparts ofthe system or systems. The controller, depending on the processingrequirements and/or the type of system, may be programmed to control anyof the processes disclosed herein, including the delivery of processinggases, temperature settings (e.g., heating and/or cooling), pressuresettings, vacuum settings, power settings, radio frequency (RF)generator settings, RF matching circuit settings, frequency settings,flow rate settings, fluid delivery settings, positional and operationsettings, wafer transfers into and out of a tool and other transfertools and/or load locks connected to or interfaced with a specificsystem.

Broadly speaking, the controller may be defined as electronics havingvarious integrated circuits, logic, memory, and/or software that receiveinstructions, issue instructions, control operation, enable cleaningoperations, enable endpoint measurements, and the like. The integratedcircuits may include chips in the form of firmware that store programinstructions, digital signal processors (DSPs), chips defined asapplication specific integrated circuits (ASICs), and/or one or moremicroprocessors, or microcontrollers that execute program instructions(e.g., software). Program instructions may be instructions communicatedto the controller in the form of various individual settings (or programfiles), defining operational parameters for carrying out a particularprocess on or for a semiconductor wafer or to a system. The operationalparameters may, in some embodiments, be part of a recipe defined byprocess engineers to accomplish one or more processing steps during thefabrication of one or more layers, materials, metals, oxides, silicon,silicon dioxide, surfaces, circuits, and/or dies of a wafer.

The controller, in some implementations, may be a part of or coupled toa computer that is integrated with, coupled to the system, otherwisenetworked to the system, or a combination thereof. For example, thecontroller may be in the “cloud” or all or a part of a fab host computersystem, which can allow for remote access of the wafer processing. Thecomputer may enable remote access to the system to monitor currentprogress of fabrication operations, examine a history of pastfabrication operations, examine trends or performance metrics from aplurality of fabrication operations, to change parameters of currentprocessing, to set processing steps to follow a current processing, orto start a new process. In some examples, a remote computer (e.g. aserver) can provide process recipes to a system over a network, whichmay include a local network or the Internet. The remote computer mayinclude a user interface that enables entry or programming of parametersand/or settings, which are then communicated to the system from theremote computer. In some examples, the controller receives instructionsin the form of data, which specify parameters for each of the processingsteps to be performed during one or more operations. It should beunderstood that the parameters may be specific to the type of process tobe performed and the type of tool that the controller is configured tointerface with or control. Thus as described above, the controller maybe distributed, such as by including one or more discrete controllersthat are networked together and working towards a common purpose, suchas the processes and controls described herein. An example of adistributed controller for such purposes would be one or more integratedcircuits on a chamber in communication with one or more integratedcircuits located remotely (such as at the platform level or as part of aremote computer) that combine to control a process on the chamber.

Without limitation, example systems may include a plasma etch chamber ormodule (employing inductively or capacitively coupled plasmas), adeposition chamber or module, a spin-rinse chamber or module, a metalplating chamber or module, a clean chamber or module, a bevel edge etchchamber or module, a physical vapor deposition (PVD) chamber or module,a chemical vapor deposition (CVD) chamber or module, an atomic layerdeposition (ALD) chamber or module, an atomic layer etch (ALE) chamberor module, an ion implantation chamber or module, a track chamber ormodule, and any other semiconductor processing systems that may beassociated or used in the fabrication and/or manufacturing ofsemiconductor wafers.

As noted above, depending on the process step or steps to be performedby the tool, the controller might communicate with one or more of othertool circuits or modules, other tool components, cluster tools, othertool interfaces, adjacent tools, neighboring tools, tools locatedthroughout a factory, a main computer, another controller, or tools usedin material transport that bring containers of wafers to and from toollocations and/or load ports in a semiconductor manufacturing factory.

Other Embodiments

Although the foregoing disclosed techniques, operations, processes,methods, systems, apparatuses, tools, films, chemistries, andcompositions have been described in detail within the context ofspecific embodiments for the purpose of promoting clarity andunderstanding, it will be apparent to one of ordinary skill in the artthat there are many alternative ways of implementing the foregoingembodiments which are within the spirit and scope of this disclosure.Accordingly, the embodiments described herein are to be viewed asillustrative of the disclosed inventive concepts rather thanrestrictively, and are not to be used as an impermissible basis forunduly limiting the scope of any claims eventually directed to thesubject matter of this disclosure.

What is claimed is:
 1. A method of generating a computational model thatrelates measured optical signals produced by optical energy interactingwith features etched on a substrate to values of a target geometricparameter of the features etched on the substrate, the methodcomprising: determining a range of the measured optical signals for usein the computational model, wherein determining the range comprises:identifying a first change in the measured optical signals in the rangedue to a variation in values of a non-target geometric parameter,identifying a second change in the measured optical signals in the rangedue to a variation in values of the target geometric parameter, anddetermining that the second change is greater than the first change;providing a training set having members with values of the opticalsignals in the range, wherein each member of the training set comprises(i) a value of the target geometric parameter of the features etched inthe substrate, and (ii) an associated optical signal produced frometched features having the value of the target geometric parameter ofthe features etched in the substrate; and producing the computationalmodel from the training set.
 2. The method of claim 1, wherein themembers of the training set further comprise values of a non-targetgeometric parameter of the features etched in the substrate.
 3. Themethod of claim 1, wherein the members of the training set are obtainedexperimentally.
 4. The method of claim 1, wherein the members of thetraining set are generated computationally.
 5. The method of claim 4,wherein the members of the training set are generated from a surfacekinetic model and an optical modelling routine.
 6. The method of claim1, wherein the training set comprises at least about 50 members.
 7. Themethod of claim 1, wherein producing the computational model from thetraining set comprises using a neural network or a regression technique.8. The method of claim 1, wherein the target geometric parameter of thefeatures etched on the substrate is an etch depth, a pitch, or an etchcritical dimension.
 9. The method of claim 1, wherein the measuredoptical signals comprise reflectance values produced from the featuresetched on the substrate.
 10. The method of claim 1, wherein the rangewhere the measured optical signals correlate less strongly with anon-target geometric parameter than with the target geometric parameteris a range of wavelengths.
 11. The method of claim 1, whereindetermining the range comprises determining variations in the rangeaccording to variations in correlation of the measured optical signalswith the target geometric parameter for different values of the targetgeometric parameter.
 12. A computational model configured to calculatevalues of a target geometric parameter for features etched on asubstrate from measured optical signals produced by optical energyinteracting with the features etched on the substrate, wherein thecomputational model was generated by the method of claim
 1. 13. A methodof determining an etch process endpoint of a target geometric parametervalue for one or more features produced on a substrate during an etchprocess, the method comprising: (a) directing incident electromagneticradiation onto the substrate; (b) measuring optical signals produced bythe incident electromagnetic radiation interacting with features beingetched on the substrate; (c) providing a subset of the measured opticalsignals, wherein the subset is defined by a range where optical signalswere determined to correlate with values of a target geometric parameterfor the features; (d) applying the subset of optical signals to a modelconfigured to predict the target geometric parameter values from themeasured optical signals, wherein the model was generated by determiningthe range where optical signals were determined to correlate with targetgeometric parameter values for features; (e) determining, from themodel, a current value of the target geometric parameter of the featuresbeing etched; (f) comparing the current value of the target geometricparameter of the features being etched to an etch process endpoint valuefor the target geometric parameter; and (g) repeating (b)-(f) until thecomparing in (f) in indicates that the current value of the targetgeometric parameter of the features being etched has reached the etchprocess endpoint value.
 14. The method of claim 13, wherein the targetgeometric parameter of the features being etched is an etch depth, apitch, or an etch critical dimension.
 15. The method of claim 13,further comprising terminating the etch process when the comparing in(e) indicates that the current value of the target geometric parameterof the features being etched has reached the etch process endpointvalue.
 16. The method of claim 13, wherein measuring optical signalsproduced in (a) comprises measuring reflectance produced from thefeatures being etched on the substrate.
 17. The method of claim 13,wherein the range defining the subset of measured optical signals in (b)is a range of wavelengths where the optical signals were determined,using a regression technique, to correlate with the target geometricparameter value for the features.
 18. The method of claim 13, whereinthe range defining the subset of measured optical signals in (b) variesbetween two repetitions of (a)-(e).
 19. The method of claim 18, whereinthe range defining the subset of measured optical signals in (b) wasdetermined to vary according to variations in correlation of the opticalsignals with the target geometric parameter for different values of thetarget geometric parameter.
 20. The method of claim 13, wherein therange defining the subset of measured optical signals in (b) is a rangewhere the optical signals were determined to correlate less stronglywith a non-target geometric parameter than the target geometricparameter.
 21. A system for etching one or more features on a substrateduring an etch process, the system comprising: an etching apparatus foretching semiconductor substrates; and a controller for controlling theoperation of the etching apparatus, the controller comprisingnon-transitory memory storing executable instructions for: (a) directingincident electromagnetic radiation to the substrate; (b) measuringoptical signals produced by optical energy interacting with featuresbeing etched on the substrate; (c) providing a subset of the measuredoptical signals, wherein the subset is defined by a range where opticalsignals were determined to correlate with values of a target geometricparameter for the features; (d) applying the subset of optical signalsto a model configured to predict the target geometric parameter valuesfrom the measured optical signals, wherein the model was generated bydetermining the range where optical signals were determined to correlatewith target geometric parameter values for features; (e) determining,from the model, a current value of the target geometric parameter of thefeatures being etched; (f) comparing the current value of the targetgeometric parameter of the features being etched to an etch processendpoint value for the target geometric parameter; and (g) repeating(b)-(f) until the comparing in (f) indicates that the current value ofthe target geometric parameter of the features being etched has reachedthe etch process endpoint value.
 22. The system of claim 21, wherein theetching apparatus comprises: a processing chamber; a substrate holderfor holding a substrate within the processing chamber; a plasmagenerator for generating a plasma within the processing chamber, theplasma generator comprising an RF power supply; one or morevalve-controlled process gas inlets for flowing one or more processgases into the processing chamber; and one or more gas outletsfluidically connected to one or more vacuum pumps for evacuating gasesfrom the processing chamber.
 23. The system of claim 21, wherein thecontroller further comprises instructions for terminating the etchprocess when the comparing in (e) indicates that the current value ofthe target geometric parameter of the features being etched has reachedthe etch process endpoint value.
 24. The system of claim 21, wherein therange defining the subset of measured optical signals in (b) is a rangeof wavelengths where the optical signals were determined, using aregression technique, to correlate with the target geometric parametervalue for the features.
 25. The system of claim 21, wherein thecontroller further comprises instructions for varying the range definingthe subset of measured optical signals in (b) between two repetitions ofexecuting instructions for (a)-(e).
 26. The system of claim 25, whereinthe range defining the subset of measured optical signals in (b) wasdetermined to vary according to variations in correlation of the opticalsignals with the target geometric parameter for different values of thetarget geometric parameter.
 27. The system of claim 21, wherein therange defining the subset of measured optical signals in (b) is a rangewhere the optical signals were determined to correlate less stronglywith a non-target geometric parameter than the target geometricparameter.
 28. The computational model of claim 12, wherein the membersof the training set used to generate the computational model furthercomprised values of a non-target geometric parameter of the featuresetched in the substrate.
 29. The computational model of claim 12,wherein the members of the training set used to generate thecomputational model were obtained experimentally.
 30. The computationalmodel of claim 12, wherein the members of the training set used togenerate the computational model were generated computationally.
 31. Thecomputational model of claim 30, wherein the members of the training setwere generated from a surface kinetic model and an optical modellingroutine.
 32. The computational model of claim 12, wherein the trainingset comprised at least about 50 members.
 33. The computational model ofclaim 12, wherein the computational model was produced from the trainingset using a neural network or a regression technique.
 34. Thecomputational model of claim 12, wherein the target geometric parameterof the features etched on the substrate is an etch depth, a pitch, or anetch critical dimension.
 35. The computational model of claim 12,wherein the optical signals comprise reflectance values produced fromthe features etched on the substrate.
 36. The computational model ofclaim 12, wherein, when generating the computational model, the rangewhere the measured optical signals correlate less strongly with anon-target geometric parameter than with the target geometric parameteris a range of wavelengths.
 37. The computational model of claim 12,wherein, when generating the computational model, determining the rangecomprises determining variations in the range according to variations incorrelation of the optical signals with the target geometric parameterfor different values of the target geometric parameter.
 38. The systemof claim 21, wherein the target geometric parameter of the featuresbeing etched is an etch depth, a pitch, or an etch critical dimension.39. The system of claim 21, wherein controller's instructions formeasuring optical signals produced in (a) comprise instructions formeasuring reflectance produced from the features being etched on thesubstrate.